{"title":"晶圆图缺陷模式识别的位置感知自监督学习","authors":"Wei Yuan;Jinda Yan;Minghao Piao","doi":"10.1109/TIM.2025.3606012","DOIUrl":null,"url":null,"abstract":"Wafer map defect pattern recognition is an indispensable component of semiconductor manufacturing, providing crucial information for identifying the root causes of defects in semiconductor production. In recent years, to address the overreliance on labeled data in supervised learning approaches, some efforts have introduced the concept of self-supervised learning into wafer map defect pattern recognition. However, these studies often ignore the significant data characteristics related to the spatial location of defect clusters on the wafer map itself. To address this issue, we designed an RingDistanceConv (RDConv) module to consider the impact of two types of position information—coordinates and distances—on wafer map defect recognition and proposed the position-aware self-supervised learning framework. Our framework achieved an accuracy of 96.41% on the WM-811K dataset with eight defect classes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Position-Aware Self-Supervised Learning for Wafer Map Defect Pattern Recognition\",\"authors\":\"Wei Yuan;Jinda Yan;Minghao Piao\",\"doi\":\"10.1109/TIM.2025.3606012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wafer map defect pattern recognition is an indispensable component of semiconductor manufacturing, providing crucial information for identifying the root causes of defects in semiconductor production. In recent years, to address the overreliance on labeled data in supervised learning approaches, some efforts have introduced the concept of self-supervised learning into wafer map defect pattern recognition. However, these studies often ignore the significant data characteristics related to the spatial location of defect clusters on the wafer map itself. To address this issue, we designed an RingDistanceConv (RDConv) module to consider the impact of two types of position information—coordinates and distances—on wafer map defect recognition and proposed the position-aware self-supervised learning framework. Our framework achieved an accuracy of 96.41% on the WM-811K dataset with eight defect classes.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-11\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11151256/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11151256/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Position-Aware Self-Supervised Learning for Wafer Map Defect Pattern Recognition
Wafer map defect pattern recognition is an indispensable component of semiconductor manufacturing, providing crucial information for identifying the root causes of defects in semiconductor production. In recent years, to address the overreliance on labeled data in supervised learning approaches, some efforts have introduced the concept of self-supervised learning into wafer map defect pattern recognition. However, these studies often ignore the significant data characteristics related to the spatial location of defect clusters on the wafer map itself. To address this issue, we designed an RingDistanceConv (RDConv) module to consider the impact of two types of position information—coordinates and distances—on wafer map defect recognition and proposed the position-aware self-supervised learning framework. Our framework achieved an accuracy of 96.41% on the WM-811K dataset with eight defect classes.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.